Metabolic profiling and pharmacokinetic studies of Baihu-Guizhi decoction in rats by UFLC-Q-TOF-MS/MS and UHPLC-Q-TRAP-MS/MS.

BACKGROUND: Baihu-Guizhi decoction (BHGZD) is a well-documented traditional Chinese Medicine (TCM) prescription that has been extensively applied to treating rheumatoid arthritis. Despite of its beneficial outcomes, the chemical constituents of BHGZD have not been fully portrayed and the in vivo absorption, distribution, metabolism, and excretion (ADME) patterns of absorbed components have never been described. METHODS: Characterization of absorbed components and in vivo biotransformation profiling of these feature compounds were based on the ultra-fast liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry (UFLC-Q-TOF-MS/MS). Furthermore, the ultra-high-performance liquid chromatography tandem ion trap quadrupole mass spectrometry (UHPLC-Q-TRAP-MS/MS) system were performed to investigate the pharmacokinetics of active ingredients from BHGZD. RESULTS: In this study, we have identified and tentatively characterized 18 feature absorbed prototype and 15 metabolites of BHGZD in rat serum and the in vivo transformation pathways of these absorbed constituents were proposed. Besides, we have established novel quantitative methodology of five crucial components of BHGZD and have monitored the pharmacokinetic behaviors of these constituents spontaneously in rat serum after BHGZD gavage. After rats received two ways of BHGZD gavage, the pharmacokinetic behaviors of each compound exhibited relatively similar behaviors, as evidenced by similar curve track as well as relatively close time to reach maximum concentration (Tmax) and half washout time (T1/2). Whereas the maximum plasma concentration (Cmax) and area under the plasma concentration-time curve (AUC) values of five analytes with multiple dosage were a bit higher than single dosage. CONCLUSION: This study added knowledge into the material basis and bio-transformation patterns of BHGZD in vivo, which would be of great value for exploring pharmacological effects and mechanism of BHGZD.

BARTweb: a web server for transcriptional regulator association analysis.

Identifying active transcriptional regulators (TRs) associating with cis-regulatory elements in the genome to regulate gene expression is a key task in gene regulation research. TR binding profiles from numerous public ChIP-seq data can be utilized for association analysis with query data for TR identification, as an alternative to DNA sequence motif analysis. However, integration of the massive ChIP-seq datasets has been a major challenge in such approaches. Here we present BARTweb, an interactive web server for identifying TRs whose genomic binding patterns associate with input genomic features, by leveraging over 13 000 public ChIP-seq datasets for human and mouse. Using an updated binding analysis for regulation of transcription (BART) algorithm, BARTweb can identify functional TRs that regulate a gene set, have a binding profile correlated with a ChIP-seq profile or are enriched in a genomic region set, without a priori information of the cell type. BARTweb can be a useful web server for performing functional analysis of gene regulation. BARTweb is freely available at http://bartweb.org and the source code is available at https://github.com/zanglab/bart2.

Detection and Comparative Analysis of Methylomic Biomarkers of Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a common autoimmune disorder influenced by both genetic and environmental factors. To investigate possible contributions of DNA methylation to the etiology of RA with minimum confounding genetic heterogeneity, we investigated genome-wide DNA methylation in disease-discordant monozygotic twin pairs. This study hypothesized that methylomic biomarkers might facilitate accurate RA detection. A comprehensive series of biomarker detection algorithms were utilized to find the best methylomic biomarkers for detecting RA patients using the methylomic data of the peripheral blood samples. The best model achieved 100.00% in accuracy (Acc) with 81 methylomic biomarkers and a 10-fold cross-validation (10FCV) strategy. Some of the methylomic biomarkers were experimentally confirmed to be associated with the onset or development of RA. It is also interesting to observe that many of the detected biomarkers were from chromosome Y, supporting the knowledge that RA has a significant gender discrepancy.

Heritability of interpack aggression in a wild pedigreed population of North American grey wolves.

Aggression is a quantitative trait deeply entwined with individual fitness. Mapping the genomic architecture underlying such traits is complicated by complex inheritance patterns, social structure, pedigree information and gene pleiotropy. Here, we leveraged the pedigree of a reintroduced population of grey wolves (Canis lupus) in Yellowstone National Park, Wyoming, USA, to examine the heritability of and the genetic variation associated with aggression. Since their reintroduction, many ecological and behavioural aspects have been documented, providing unmatched records of aggressive behaviour across multiple generations of a wild population of wolves. Using a linear mixed model, a robust genetic relationship matrix, 12,288 single nucleotide polymorphisms (SNPs) and 111 wolves, we estimated the SNP-based heritability of aggression to be 37% and an additional 14% of the phenotypic variation explained by shared environmental exposures. We identified 598 SNP genotypes from 425 grey wolves to resolve a consensus pedigree that was included in a heritability analysis of 141 individuals with SNP genotype, metadata and aggression data. The pedigree-based heritability estimate for aggression is 14%, and an additional 16% of the phenotypic variation was explained by shared environmental exposures. We find strong effects of breeding status and relative pack size on aggression. Through an integrative approach, these results provide a framework for understanding the genetic architecture of a complex trait that influences individual fitness, with linkages to reproduction, in a social carnivore. Along with a few other studies, we show here the incredible utility of a pedigreed natural population for dissecting a complex, fitness-related behavioural trait.

icSHAPE-pipe: A comprehensive toolkit for icSHAPE data analysis and evaluation.

RNA molecules have the intrinsic ability to fold into complex structures that are important in regulating many biological processes, including transcription, translation, processing and degradation. However, our knowledge for RNA structures remains very limited. Previously, we developed icSHAPE, a high-throughput method to probe single-stranded RNA nucleotide in cells. To recover the structural profile of an RNA or a transcript by icSHAPE, it is essential to accurately calculate an icSHAPE reactivity as the RNA structure score for each base. Here, we present icSHAPE-pipe, a comprehensive toolkit for the analysis of RNA structure sequencing data obtained from icSHAPE experiments. Compared to the original icSHAPE data processing protocol, icSHAPE-pipe calculates RNA structural information with higher accuracy and achieves higher coverage of the transcriptome. In addition, icSHAPE-pipe can perform quality control, and generate reports on sequencing data and the statistics of results. In sum, icSHAPE-pipe provides a convenient workflow for researchers to analyze RNA structural data from icSHAPE sequencing experiments.

sefOri: selecting the best-engineered sequence features to predict DNA replication origins.

MOTIVATION: Cell divisions start from replicating the double-stranded DNA, and the DNA replication process needs to be precisely regulated both spatially and temporally. The DNA is replicated starting from the DNA replication origins. A few successful prediction models were generated based on the assumption that the DNA replication origin regions have sequence level features like physicochemical properties significantly different from the other DNA regions. RESULTS: This study proposed a feature selection procedure to further refine the classification model of the DNA replication origins. The experimental data demonstrated that as large as 26% improvement in the prediction accuracy may be achieved on the yeast Saccharomyces cerevisiae. Moreover, the prediction accuracies of the DNA replication origins were improved for all the four yeast genomes investigated in this study. AVAILABILITY AND IMPLEMENTATION: The software sefOri version 1.0 was available at http://www.healthinformaticslab.org/supp/resources.php. An online server was also provided for the convenience of the users, and its web link may be found in the above-mentioned web page. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detecting Methylomic Biomarkers of Pediatric Autism in the Peripheral Blood Leukocytes.

Autism was a spectrum of multiple complex diseases that required an interdisciplinary group of experts to make a diagnostic decision. Both genetic and environmental factors play essential roles in causing the onset of Autism. Therefore, this study hypothesized that methylomic biomarkers may facilitate the accurate Autism detection. A comprehensive series of biomarker detection algorithms were utilized to find the best methylomic biomarkers for the Autism detection using the methylomic data of the peripheral blood samples. The best model achieved 99.70% in accuracy with 678 methylomic biomarkers and a tenfold cross validation strategy. Some of the methylomic biomarkers were experimentally confirmed to be associated with the onset or development of Autism.